feat(api): Add options for supporting various embedding models (#1192)

We need to support:
- asymmetric embedding models (#934)
- truncation policies (#933)
- varying dimensional output (#932) 

## Test Plan

```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
   --inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$  pytest -s -v -k together test_embeddings.py \
   --inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
   --inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -23,14 +23,20 @@ from llama_stack.apis.inference import (
     CompletionResponse,
     CompletionResponseStreamChunk,
     EmbeddingsResponse,
+    EmbeddingTaskType,
     Inference,
     LogProbConfig,
     Message,
     ResponseFormat,
+    TextTruncation,
     ToolChoice,
     ToolConfig,
 )
-from llama_stack.models.llama.datatypes import SamplingParams, ToolDefinition, ToolPromptFormat
+from llama_stack.models.llama.datatypes import (
+    SamplingParams,
+    ToolDefinition,
+    ToolPromptFormat,
+)
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
 )
@@ -122,6 +128,9 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         self,
         model_id: str,
         contents: List[str] | List[InterleavedContentItem],
+        text_truncation: Optional[TextTruncation] = TextTruncation.none,
+        output_dimension: Optional[int] = None,
+        task_type: Optional[EmbeddingTaskType] = None,
     ) -> EmbeddingsResponse:
         if any(content_has_media(content) for content in contents):
             raise NotImplementedError("Media is not supported")